Why You Should Design Your Data Hub Top-Down vs. Bottom-up

CIOs and data professionals who want to select the most effective and ideal design formula for data repositories.

ReadITQuik: The proliferation of new IT trends, shifts, applications, and big data goals is creating the need for strong central repositories more than ever. This strategic evaluation of an architecture choice is going to have considerable outcomes on business intelligence and analytics, business continuity, as well as on revenues. Dive into some best practices with this whitepaper.

Takeaways

Take a look at the new-found role and significance of central repositories of data.

Understand what a data hub is and why its design matters? A quick rewind button on enterprise service bus (ESB) and erstwhile architectural stacks help here.

Note the gaps that play out in virtualization-only and schema-less architectures. Be careful about volatility and duplication, for instance.

Understand some expectations from a data hub that crop up in the modern business intelligence and analytics world. Orchestration, micro service exposure, rapid response, agility, and flexibility surface here.

Know the key elements of a data hub design. Data ingestion, exposure, and governance make it to this list too.

What is your data hub like? A camel, a horse, or a llama-corn? Read this whitepaper to find out.

RIQ PERSPECTIVE

Apart from design analysis for data hubs, organizations also need to assess the emergence of alternatives/complementary models like federation or data lakes. They can be picked on parameters of storage, cost of storing, processing burden, movement hassles, the maturity of a model, querying speed, and the dominance of relational databases.

What will also matter is the proximity of data to a raw state. This is where the user needs will have to be defined and incorporated well. Also, if data is still housed in silos, fragmented, or incompatible; then it is a red flag that goes beyond the question of architecture choice that we are dwelling upon.